Description

Reinforcement Learning: An Introduction, Second Edition by Richard S. Sutton and Andrew G. Barto is a foundational textbook on reinforcement learning and one of the standard references in the field of artificial intelligence and machine learning. It explains how agents learn to make decisions through interaction with their environment and feedback from rewards.

The book covers Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, function approximation, policy-gradient methods, and deep reinforcement learning concepts, making it useful for students, researchers, and AI professionals.

Key Features

• Edition: Second Edition
• Authors: Richard S. Sutton & Andrew G. Barto
• Publisher: MIT Press
• Series: Adaptive Computation and Machine Learning
• Subject: Reinforcement Learning / Artificial Intelligence
• Topics: Dynamic programming, Monte Carlo methods, temporal-difference learning, function approximation, policy methods, and reinforcement learning algorithms
• Ideal For: AI students, machine learning researchers, software engineers, and data scientists
• Use: Machine learning study, research, and AI development

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Learn More: Reinforcement Learning – Wikipedia